Make your first machine learning model with the TensorFlow framework. Make an Android app that can analyze and predict handwritten digit data. Make an advanced app with the MNIST database of digits. Make an app that can predict the weather. Description This is our epic course with 5 projects in artificial intelligence and machine learning: 01.

With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications. The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.

Imagine that we are working for a real estate agency and our role is to estimate price of apartment renting in a different parts of the New York City. In classic machine learning approach we work through those variables and build model for prediction of a price. We will consider the last example and learn how to retrieve spatial information using GeoPandas package and publicly available geographical datasets. Data for this article is shared in Kaggle. Data is also available in the blogpost repository.

TORONTO, March 19, 2021 /PRNewswire-PRWeb/ -- Which artificial intelligence method will work best with your image data? Image analysis is an essential part of digital pathology, from research and discovery of targets and biomarkers to understanding the tumor microenvironment to development of novel therapeutics. Whether you need to perform simple tasks or complex analysis of multiplex markers, artificial intelligence can improve performance and facilitate your analysis tasks to unlock the information hidden in image data. During the last years, deep learning algorithms have revolutionized the quality of analysis and allowed accurate assessment of highly heterogeneous and previously challenging tissue structures. In order to best utilize the capabilities that AI methods offer, however, it is important to understand what the terms "artificial intelligence" (AI), "machine learning" (ML), and "deep learning" (DL) really refer to when it comes to image analysis.

Tensorflow.js: Build an Image Classifier using Tensorflow Learn how to train a machine learning model and classify images using Tensorflow.js How to train a machine learning model using Google's Teachable Machine The ability to train and deploy an image model is incredibly powerful. You could apply this skill to industries such as Healthcare, Business, Teaching, and much more. Or you could even sort your own personal photos using these techniques! For this course, we will take a dataset from Kaggle showing individuals who are wearing face masks and not wearing face masks.

Data Analyst - ETL/SSIS/SQL/PowerBI Learn to extract,transform, and analyse data. Description Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. The ability to pay attention to detail, communicate well and be highly organised are essential skills for data analysts. They not only need to understand the data, but be able to provide insight and analysis through clear visual, written and verbal communication. A common problem that organizations face is how to gathering data from multiple sources, in multiple formats, and move it to one or more data stores.

This tutorial is part of the 2021 Call for Code Global Challenge. In this tutorial, learn how to pull 40 years' of global satellite-based soil moisture data from the European Commission, then train a model to compute moisture trends to identify regions that have a high probability to dry out and have droughts. Copernicus is the European Union's Earth observation program, looking at our planet and its environment. It offers information services that draw from satellite Earth observation and in-situ (non-space) data. Vast amounts of global data from satellites and ground-based, airborne, and seaborne measurement systems provide information.

In this tutorial, you will learn the proper ways to extract your personal data from your Linkedin account and use Python to analyze and draw useful insights from it. If you don't have a Linkedin account, please run as fast as you can to Linkedin page to create one. Actually it's not a good habit to not have a Linkedin account in this modern world…lol Linkedin is one of the biggest social network out there, and the chances are you are proud Linkedin member (if not create one now-please). Linkedin gives you access you to your data and you can download and analyze this data to draw insights from it. Linkedin has a clear guide as to how to download your data.

GMRES is a powerful numerical solver used to find solutions to extremely large systems of linear equations. These systems of equations appear in many applications in science and engineering. Here we demonstrate a real-time machine learning algorithm that can be used to accelerate the time-to-solution for GMRES. Our framework is novel in that is integrates the deep learning algorithm in an in situ fashion: the AI-accelerator gradually learns how to optimizes the time to solution without requiring user input (such as a pre-trained data set). We describe how our algorithm collects data and optimizes GMRES. We demonstrate our algorithm by implementing an accelerated (MLGMRES) solver in Python. We then use MLGMRES to accelerate a solver for the Poisson equation -- a class of linear problems that appears in may applications. Informed by the properties of formal solutions to the Poisson equation, we test the performance of different neural networks. Our key takeaway is that networks which are capable of learning non-local relationships perform well, without needing to be scaled with the input problem size, making them good candidates for the extremely large problems encountered in high-performance computing. For the inputs studied, our method provides a roughly 2$\times$ acceleration.

This multiple session tutorial is designed to train researchers and practitioners to begin analyzing synchrophasor (i.e., PMU) and point on wave data. The course covers concepts from power engineering and data science, and will show attendees how to develop efficient workflows for analyzing and visualizing time series data at scale. The first day of the course will cover foundational concepts from power systems engineering, and will relate PMU data to physical properties of the grid. The session will discuss phasor calculation, and methods for using phasor data to compute frequency. We will close with a summary of best practices and lessons learned from using PMU data in industry.